A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks by Jose Abreu, Roberto L. Oliveira, Joao V. Fonseca Neto

“…This study proposes a comprehensive approach to determine the impact point prediction of ballistic rocket payloads. This approach combines tracking algorithms that are based on stochastic estimators with artificial neural network (ANN) models. …”
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Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning by Sarah Dandou, Kriti Amin, Véronique D’Hondt, Jérôme Solassol, Olivier Dereure, Peter J. Coopman, Ovidiu Radulescu, Holger Fröhlich, Romain M. Larive

“…We used machine learning algorithms and pre-processed genomic data to test whether they could contain useful information to improve the progression-free survival (PFS) prediction. …”
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Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach by Rafael Travincas, Maria Paula Mendes, Isabel Torres, Inês Flores-Colen

“…This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. …”
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Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning by Po-Ting Pan, Yamine Bouzembrak, Miguel Quemada, Bedir Tekinerdogan

“…Therefore, this study integrated PlanetScope satellite images with weather data while adopting three ML algorithms, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to predict NNI in Spain from 2018 to 2019. …”
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Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process by Oluwole Abiodun Raimi, Bong-Kee Lee

“…To evaluate the prediction accuracy and capability of the proposed method, three different algorithms, namely Levenberg–Marquardt (lm), BGFS quasi-Newton (bfg), and scale conjugate gradient (scg), were included in the proposed model. …”
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Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

“…To respond to and address this challenge more effectively, a predictive, data-driven approach is essential. This study applies machine learning techniques to forecast the number of people living in poverty in 2024 at the district/city level, utilizing socio-economic data from 2019 to 2023 provided by the Central Bureau of Statistics (BPS). …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…The introduction of such risk stratification algorithms into practice will increase the accuracy and quality of CVR prediction and optimize the system of preventive measures.…”
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A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments by Jae Kwon Bae, Jinhwa Kim

“…This paper builds several personal credit rating prediction models based on the UDM and benchmarks their performance against other models which employ logistic regression (LR), Bayesian style frequency matrix (BFM), multilayer perceptron (MLP), classification tree methods (C5.0), and neural network rule extraction (NR) algorithms. …”
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Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations by Ayoub Jadouli, Chaker El El Amrani

“…We compile essential environmental indicators and employ state-of-the-art machine learning (ML) and deep learning (DL) algorithms to predict next-day wildfire occurrences. …”
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Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing. by Sven B Gould, Jonas Magiera, Carolina García García, Parth K Raval

“…Hence, hundreds of studies make use of algorithms that predict a localisation based on a protein's sequence. …”
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Link prediction accuracy on real-world networks under non-uniform missing-edge patterns. by Xie He, Amir Ghasemian, Eun Lee, Alice C Schwarze, Aaron Clauset, Peter J Mucha

“…To investigate the impact of different missing-edge patterns on link prediction accuracy, we employ 9 link prediction algorithms from 4 different families to analyze 20 different missing-edge patterns that we categorize into 5 groups. …”
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Predicting mortality risk in Alzheimer’s disease using machine learning based on lifestyle and physical activity by Ruihong Tang, Liang Tan, Xia Chen, Zhengwu Shan, Zhao Zhang, Panpan Cheng

“…Lifestyle and physical activity levels were identified as significant predictors influencing mortality risk. ML algorithms, notably RSF, effectively predict mortality risk in AD patients, demonstrating clear advantages over traditional statistical models. …”
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Setting the standard for machine learning in phase field prediction: a benchmark dataset and baseline metrics by Laura Hannemose Rieger, Klemen Zelič, Igor Mele, Tomaž Katrašnik, Arghya Bhowmik

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Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis by Zohreh Javanmard, Saba Zarean Shahraki, Kosar Safari, Abbas Omidi, Sadaf Raoufi, Mahsa Rajabi, Mohammad Esmaeil Akbari, Mehrad Aria

“…Original articles applying ML algorithms for BC survival prediction using clinical data were included. …”
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Machine Learning-Based Prediction of Ecosystem-Scale CO₂ Flux Measurements by Jeffrey Uyekawa, John Leland, Darby Bergl, Yujie Liu, Andrew D. Richardson, Benjamin Lucas

“…In this study, we use machine learning algorithms to predict CO₂ flux measurements at NEON sites (a subset of Ameriflux sites), creating a model to gap-fill measurements when sites are down or replace measurements when they are incorrect. …”
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…Based on the optimal combination, six machine learning algorithms were used to construct an early BSI risk prediction model. …”
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Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting by Vijayalakshmi S, John A, Sunder R, Senthilkumar Mohan, Sweta Bhattacharya, Rajesh Kaluri, Guang Feng, Usman Tariq

“…The main novelty of this work is multi-modal prediction algorithm for breast cancer prediction is proposed. …”
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An Updated Systematic Review on Asthma Exacerbation Risk Prediction Models Between 2017 and 2023: Risk of Bias and Applicability by Liu A, Zhang Y, Yadav CP, Chen W

“…Anqi Liu, Yue Zhang, Chandra Prakash Yadav, Wenjia Chen Saw Swee Hock School of Public Health, National University of Singapore, SingaporeCorrespondence: Wenjia Chen, Tahir Foundation Building, National University of Singapore, 12 Science Drive 2, #10-01, Singapore, 117549, Email wenjiach@nus.edu.sgBackground: Accurate risk prediction of exacerbations in asthma patients promotes personalized asthma management.Objective: This systematic review aimed to provide an update and critically appraise the quality and usability of asthma exacerbation prediction models which were developed since 2017.Methods: In the Embase and PubMed databases, we performed a systematic search for studies published in English between May 2017 and August 2023, and identified peer-reviewed publications regarding the development of prognostic prediction models for the risk of asthma exacerbations in adult patients with asthma. …”
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Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

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Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques by Ahmed I. Saleh, Nabil S. Mahmoud, Fikry A. Salem, Mohamed Ghannam

“…Abstract This study investigates the use of machine learning (ML) models to predict the ultimate load capacity of demountable shear connectors in steel–concrete composite structures. …”
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